• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.111-117
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• 2023

The aim of this study is to present a practical teaching plan for liberal arts English classes that target foreign students. Foreign learners who do not have Korean language proficiency at the university level may struggle to understand the contents of liberal arts classes conducted by Korean language professors. In this study, six topics were selected (K-culture, Online game, Harry Potter, Disney, Marvel, DC) and topic-centered participatory class activities using various media were developed. A questionnaire was conducted to analyze learners' attitudes toward and perceptions regarding topic-oriented classes. It showed that learners' satisfaction with topic-based classes was high (75%), and the reasons for this high level of satisfaction were the instructors' caring attitudes, the comfortable class atmosphere, and the fun learners had in class. Learners also reported high satisfaction with various participatory class activities (81.9%), citing the learning benefits, their increased interest and motivation, and the efficiency of participatory classes. As globalization continues to increase the number of foreign students in South Korea, the need to develop realistic class plans and various class activities that are suitable for them is becoming more and more urgent.

• Smart Media Journal
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• v.4 no.3
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• Information Systems Review
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• v.21 no.2
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• pp.117-137
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• 2019

In the Big Data era, data science has become popular with the production of numerous data in various domains, and the power of data has become a competitive power. There is a growing interest in unstructured data, which accounts for more than 80% of the world's data. Along with the everyday use of social media, most of the unstructured data is in the form of text data and plays an important role in various areas such as marketing, finance, and distribution. However, text mining using social media is difficult to access and difficult to use compared to data mining using numerical data. Thus, this study aims to develop Korean Natural Language Application (KoALA) as an integrated application for easy and handy social media text mining without relying on programming language or high-level hardware or solution. KoALA is a specialized application for social media text mining. It is an integrated application that can analyze both Korean and English. KoALA handles the entire process from data collection to preprocessing, analysis and visualization. This paper describes the process of designing, implementing, and applying KoALA applications using the design science methodology. Lastly, we will discuss practical use of KoALA through a block-chain business case. Through this paper, we hope to popularize social media text mining and utilize it for practical and academic use in various domains.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• Cross-Cultural Studies
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• v.23
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• pp.243-264
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• 2011

The results of 2000 Population Census explains the context of a series of incidents happening in the Latino television broadcasting market recently. That is, the rapid growth of Latino population so fast as overtaking the Black population has needed media as a means of Latino's own social interests and communication. In this context, the television broadcasting market as a marketing means of capitals targeting for the Latinos has experienced more rapid changes. In other words, there has been some changes in the Latino television broadcasting market which divided by two major networks, Univision and Telemundo. It was 1970s when Latino media started to be considered as an important framework to understand the problems of the Latinos in American society. Experiencing the human rights movement of the 1960s, the Latino communities' sense of identity realized the importance of media as an expression of themselves from the interest on the factors which directly determine the quality of their life such as the immigration, education, health, and employment. The anglo media plays a role in introducing the Latinos and forming the images of the Latinos to the non-Latinos. It can be possible to criticize that the anglo media propagates the unilateral image of the Latinos by the mainstream white society, the stereotyped images of the Latinos. The spanish media targeting for the Latinos has grown continuously, combining the inside needs of forming the identity of the Latinos and communication and outside needs of commercialism. On the other hand, the needs for the programs based on the American Latinos has been increased, along with the increase of the Latino media based on the dual languages or English. This paper reviewed the history of the Latino media briefly, and then examined the relationship between the Latinos and the media through the television broadcasting which influence the Latino's everyday life enormously.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.1-19
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• 2000

With rapid economic development, the emphasis of the public health movement in Korea has shifted towards addressing the burden of chronic disease. With this shift in direction comes a greater focus on health behaviour and the need for planning models to assist in lifestyle modification programs. The Health Belief Model (HBM), which originated in the US, has generated more research than any other theoretical approach to describe and predict the health behaviour of individuals. In recent years it has been applied in many different cultures and modifications have been suggested to accommodate different cultures. Given the centrality of language and culture, any attempts to use models of health behaviour developed in a different culture, must be studied and tested for local applicability. The paper reviews the applicability and suitability of the HBM in Korea, in the context of the Korean language and culture. The HBM has been used in Korea for almost three decades. The predictability of the HBM has varied in Korean studies as in other cultures. Overall, this literature review indicates that the HBM has been found applicable in predicting health and illness behaviours by Korean people. However if the HBM is used in a Korean context, the acquisition of health knowledge is an important consideration. Most new knowledge in the health sciences is originally published in English and less frequently in another foreign language. Most health knowledge in Korea is acquired through the media or from health professionals and its acquisition often involves translation from the original. The selection of articles for translation and the accuracy of translation into language acceptable in the Korean culture become important determinants of health knowledge. As such translation becomes an important part of the context of the HBM. In this paper modifications to the HBM are suggested to accommodate the issues of language and knowledge in Korea.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.427-436
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• 2021

The purpose of this study is to implement a machine learning language model that learns literary texts. Literary texts have an important characteristic that pairs of question-and-answer are not frequently clearly distinguished. Also, literary texts consist of pronouns, figurative expressions, soliloquies, etc. They hinder the necessity of machine learning using literary texts by making it difficult to learn algorithms. Algorithms that learn literary texts can show more human-friendly interactions than algorithms that learn general sentences. For this goal, this paper proposes three text correction tasks that must be preceded in researches using literary texts for machine learning language model: pronoun processing, dialogue pair expansion, and data amplification. Learning data for artificial intelligence should have clear meanings to facilitate machine learning and to ensure high effectiveness. The introduction of special genres of texts such as literature into natural language processing research is expected not only to expand the learning area of machine learning, but to show a new language learning method.

• Journal of English Language & Literature
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• v.55 no.6
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• pp.1163-1191
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• 2009

As many critics do, this article starts from the premise that Charles Waddell Chesnutt wrote The Conjure Woman with a distinct socio-ethical view to ameliorating white readers' racism. For this purpose of social activism, first, the author uses a racially submissive genre and narrator- antebellum plantation-dialect fiction and an old ex-slave Julius-in order to win the attention of white racists, who constituted the majority of the reading public of postbellum America. Chesnutt then allows this seemingly submissive ex-slave consecutively to wage narrative battles against a Northern white capitalist, John. This fiction's structure is thus based on interracial narrative conflict. Granted, the result of these narrative battles is Julius's defeat. Even though he sometimes has narrative success through his manipulation of either his white female auditor's sentimentalism or the white capitalist's racial prejudice, it does not lead to any fundamental change in the white audience members' awareness: John still regards Julius's tacitly reformoriented tales merely as nonsensical ghost stories invented by the absurd imagination of a subservient, entertaining, and exploitable black coachman. Admitting his defeat, Julius relinquishes his original goal of deterring John's capitalist exploitation of both racial Others and the natural environment of the South and finally decides to serve the economic power of white capitalism. This self-defeating conclusion, however, should not be identified with Chesnutt's failure as an author. Rather, it should be understood as an interim result of the black author's earnest experiment with literary media best suited to his reform project. In fact, this narrative failure reveals Chesnutt's accurate diagnosis of the postbellum literary world: a black voice is still feebly heard and even easily buried by the whites' capitalist ambition and consequently intensifying racism. Conclusively, Julius's narrative failure should be positively evaluated as Chesnutt's one step further in his gradual and lifelong progress to a narrative goopher effectively to engage whites' imagination and sympathy for a vision of equal interracial coexistence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1321-1329
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• 2019

With the development of social information networks, the marketing methods are also changing in various ways. Unlike successful marketing methods based on existing celebrities and financial support, Influencer-based marketing is a big trend and very famous. In this paper, we first extract influencer features from more than 54 YouTube channels using the multi-dimensional qualitative analysis based on the meta information and comment data analysis of YouTube, model representative themes to maximize a personalized video satisfaction. Plus, the purpose of this study is to provide supplementary means for the successful promotion and marketing by creating and distributing videos of new items by referring to the existing Influencer features. For that we assume all comments of various videos for each channel as each document, TF-IDF (Term Frequency and Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) algorithms are applied to maximize performance of the proposed scheme. Based on the performance evaluation, we proved the proposed scheme is better than other schemes.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.50-59
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• 2021

To understand the research trends of English education in Korea for the last 20 years from 2000 to 2019, 12 major academic journals in Korea in the field of English education were selected, and bibliographic information of 7,329 articles published in these journals were collected and analyzed. The total number of articles increased from the 2000s to the first half of the 2010s, but decreased somewhat in the late 2010s and the number of publications by journal has become similar. These results show that the overall influence of English education journals has decreased and then leveled in terms of quantity. Next, 34 topics were extracted by applying latent Dirichlet allocation (LDA) topic modeling using the English abstract of the articles. Teacher, word, culture/media, and grammar appeared as topics that were highly studied. Topics such as word, vocabulary, and testing and evaluation appeared through unique keywords, and various topics related to learner factors emerged, becoming topics of interest in English education research. Then, topics were analyzed to determine which ones were rising or falling in frequency. As a result of this analysis, qualitative research, vocabulary, learner factor, and testing were found to be rising topics, while falling topics included CALL, language, teaching, and grammar. This change in research topics shows that research interests in the field of English education are shifting from static research topics to data-driven and dynamic research topics.